Asset management is an important focus area for companies, such as large enterprises with multiple geographically distributed industrial plants and facilities. Such enterprises most typically include oil and gas field production and processing, refineries and petrochemical plants. A common type of problem in such organizations is the performance monitoring of assets, such as heat exchangers, heaters or pumps, which are usually used in quite large numbers.
The companies usually have some basic asset monitoring functions deployed as part of a general management capabilities or dedicated asset monitoring solutions. The most typical approach involves regular calculation of equipment-specific performance measures (e.g. heat transfer coefficient of a heat exchanger, or efficiency of a heater) and then monitoring of these calculated measures for all assets of the same type. This typically leads to the identification of a list of top ten “worst actors”, and consequently, responding to potential problems associated with these worst performing assets in a reactive manner.
One prior method for automatically monitoring the performance of a single piece of equipment includes compiling current operating conditions associated with current performance measure conditions (PMc). A historical database including a plurality of stored operating conditions and associated stored performance measure (PM*) is searched, each stored operating condition including at least one stored sensor reading, wherein at least one similar operating condition is identified in the search using distances between the current operating conditions and the stored operating conditions. The performance measure (PM*) associated with the similar operating condition is fit to generate a regression model. The regression model is applied to the current operating condition to generate an estimate for the performance measure for the current operating condition (PMe). A difference between PMc and PMe is computed. The first difference is compared to a predetermined threshold and a warning is automatically generated if the first difference has a value greater than the predetermined threshold.